Content-analysis of scientific and practical research areas in social pharmacy
Bibliographic record
Abstract
In many countries, social pharmacy, as a research area, is well developed, providing international and interdisciplinary collaboration in the context of discussing and disseminating health-related knowledge. Taking into account the European integration processes of reforming the domestic pharmaceutical sector of healthcare, it is important to study the international experience of the formation of social pharmacy as the direction of scientific research.Aim: analysis of scientific and practical areas of social pharmacy research.Materials and methods. The research of scientific publications of the International Journal “Research in Social and Administrative Pharmacy” for the period of 2012–2018 has been conducted using content analysisResults. The most authoritative scientific publication that covers all aspects of social pharmacy is the scientific journal “Research in Social and Administrative Pharmacy”, the editorial board of which includes scientists from The United States, Australia, The United Kingdom, Canada, Portugal, Egypt, Brazil, Northern Ireland, Finland, Kosovo, New Zealand, Estonia. It is established that, for the most part, the results of original research are published (67.7 % of all publications); in addition, the publication contains review articles, brief research, proposed models. The most widespread categories in the field of social pharmacy are community pharmacy, pharmacist, pharmaceutical service, medication adherence, communication between “pharmacists – patients – physician”, health literacy, pharmacotherapy safety, self-medication etc. Published materials cover study of most socially significant diseases (hypertension, asthma, diabetes, neurological diseases) with all categories of the population (children, adults, elderly, pregnant, rural population etc.) in the vast majority of countries of the world (North and South America, European countries, some African countries, Middle and Far East, Australia and New Zealand).Conclusions. Priority areas of social pharmacy research focus on social, organizational, economic, legal, psychological, and communication aspects that affect the effectiveness of pharmaceutical care and indicators of patient's life quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.094 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.042 | 0.050 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".